Generative AI vs. Traditional Databases: Insights from Industrial Engineering Applications

Jose E. Naranjo (), Maria M. Llumiquinga, Washington D. Vaca and Cristian X. Espin
Additional contact information
Jose E. Naranjo: Facultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de Cotopaxi, Ave. Simón Rodríguez, Latacunga 050102, Ecuador
Maria M. Llumiquinga: Facultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de Cotopaxi, Ave. Simón Rodríguez, Latacunga 050102, Ecuador
Washington D. Vaca: Facultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de Cotopaxi, Ave. Simón Rodríguez, Latacunga 050102, Ecuador
Cristian X. Espin: Facultad de Ciencias de la Ingeniería y Aplicadas, Universidad Técnica de Cotopaxi, Ave. Simón Rodríguez, Latacunga 050102, Ecuador

Publications, 2025, vol. 13, issue 2, 1-25

Abstract: This study evaluates the efficiency and accuracy of Generative AI (GAI) tools, specifically ChatGPT and Gemini, in comparison with traditional academic databases for industrial engineering research. It was conducted in two phases. First, a survey was administered to 101 students to assess their familiarity with GAIs and the most commonly used tools in their academic field. Second, an assessment of the quality of the information provided by GAIs was carried out, in which 11 industrial engineering professors participated as evaluators. The study focuses on the query process, response times, and information accuracy, using a structured methodology that includes predefined prompts, expert validation, and statistical analysis. A comparative assessment was conducted through standardized search workflows developed using the Bizagi tool, ensuring consistency in the evaluation of both approaches. Results demonstrate that GAIs significantly reduce query response times compared to conventional databases, although the accuracy and completeness of responses require careful validation. A Chi-Square analysis was performed to statistically assess accuracy differences, revealing no significant disparities between the two AI tools. While GAIs offer efficiency advantages, conventional databases remain essential for in-depth literature searches requiring high levels of precision. These findings highlight the potential and limitations of GAIs in academic research, providing insights into their optimal application in industrial engineering education.

Keywords: generative artificial intelligence; comparison; process; Bizagi; ChatGPT; Gemini; industrial engineering (search for similar items in EconPapers)
JEL-codes: A2 D83 L82 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2304-6775/13/2/14/pdf (application/pdf)
https://www.mdpi.com/2304-6775/13/2/14/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jpubli:v:13:y:2025:i:2:p:14-:d:1619769

Access Statistics for this article

Publications is currently edited by Ms. Jennifer Zhang

More articles in Publications from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().